Including Land Cover Research Articles

Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression

Global greening over the past 30 years since 1980s has been confirmed by numerous studies. However, a single-dimensional indicator and non-spatial modelling approaches might exacerbate uncertainties in our understanding of global change. Thus, comprehensive monitoring for vegetation’s various properties and spatially explicit models are required. In this study, we used the newest enhanced vegetation index (EVI) products of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 to detect the inconsistency trend of annual peak and average global vegetation growth using the Mann–Kendall test method. We explored the climatic factors that affect vegetation growth change from 2001 to 2018 using the spatial lag model (SLM), spatial error model (SEM) and geographically weighted regression model (GWR). The results showed that EVImax and EVImean in global vegetated areas consistently showed linear increasing trends during 2001–2018, with the global averaged trend of 0.0022 yr−1 (p < 0.05) and 0.0030 yr−1 (p < 0.05). Greening mainly occurred in the croplands and forests of China, India, North America and Europe, while browning was almost in the grasslands of Brazil and Africa (18.16% vs. 3.08% and 40.73% vs. 2.45%). In addition, 32.47% of the global vegetated area experienced inconsistent trends in EVImax and EVImean. Overall, precipitation and mean temperature had positive impacts on vegetation variation, while potential evapotranspiration and vapour pressure had negative impacts. The GWR revealed that the responses of EVI to climate change were inconsistent in an arid or humid area, in cropland or grassland. Climate change could affect vegetation characteristics by changing plant phenology, consequently rendering the inconsistency between peak and mean greening. In addition, anthropogenic activities, including land cover change and land use management, also could lead to the differences between annual peak and mean vegetation variations.

Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model

Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily mean Ta from satellite-based land surface temperature (Ts) over Sweden during 2001–2019 at a high spatial resolution of 1 × 1 km2. The ensemble model incorporated four base models, including a generalized additive model (GAM), a generalized additive mixed model (GAMM), and two machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. Various spatial predictors were included as adjustment variables in all the base models, including land cover type, normalized difference vegetation index (NDVI), and elevation. The ensemble model showed high performance with an overall R2 of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Although each base model performed well, the two machine learning models (RF [R2 = 0.97], XGBoost [R2 = 0.98]) had better performance than the two regression models (GAM [R2 = 0.95], GAMM [R2 = 0.96]). In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, NDVI, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment in future epidemiological studies.

A spatial model of forest and land fire vulnerability level in the Dairi District, North Sumatra, Indonesia

Abstract. Thoha AS, Triani H. 2021. A spatial model of forest and land fire vulnerability level in the Dairi District, North Sumatra, Indonesia. Biodiversitas 22: 3319-3326. Fires often occur every dry season and have a significant impact on ecosystems and human activities. One of the important roles in reducing the risk of forest and land fires is the availability of updated vulnerability level maps in all vulnerable areas. The objective of this study was to determine the relationship between the driving factors for forest and land fires and hotspots and to obtain a spatial model of the distribution of vulnerability to forest and land fires in the Dairi District of North Sumatra Province. This study uses a composite mapping analysis method to obtain a spatial model and the distribution of vulnerable areas to forest and land fires. Six variables in the form of maps were used in building the model, including land cover, population density, distance from the road, distance from the river, and distance from the settlement. This study showed that the most important variable for vulnerability level model of forest and land fires was the distance from the settlement. This study also found that open land, the farthest distance to the road, the farthest distance to the river, the farthest distance to the settlement, and the densest population were the driving factors for increased fire activity. The spatial model of the vulnerability level to forest and land fires in Dairi District was Y = 0.022X1 + 0.214X2 + 0.113X3 + 0.482X4 + 0.169X5. Land cover having high-very high vulnerability level belonged to open land dominated by grass. The largest areas with a high-very high forest fire vulnerability level in Dairi District were spread over Tanah Pinem Sub-district.

Measurement of Thermal Comfort in Urban Public Spaces Semarang, Indonesia

Indonesia reported a maximum annual temperature rise of 0.3°C in urban regions. Semarang, the largest metropolitan city in the province of Central Java, is also experiencing an increase in temperature due to climate change therefore activities in urban public spaces are disrupted due to the absence of a comfortable temperature. Urban design elements, including land cover materials, road geometry, vegetation and traffic frequency expressed significant effects on micro-climate. Measurement of Thermal Comfort in Urban Public Spaces Semarang was carried out s at the micro level as an old historical district The Old Town and Chinatown. This increment indeed influences thermal comfort level in its outdoor environments which are important for comfortability of outdoor activity. This study aims to analyse surface temperature through Thermal Comfort Measurement. Data was obtained by measuring air temperature, wind speed and humidity in the morning, afternoon, and evening. Inverse distance weighted (IDW), thermal comfort calculations and micro-climate model were employed to evaluate existing physical conditions of these settlements. The results showed both Old Town and Chinatown observed thermal comfort value above 27°C and are categorized as uncomfortable for outdoor activities. This research is contributing to the need to further develop public spaces to potentially adapt to environmental changes.

Quantifying 3D building form effects on urban land surface temperature and modeling seasonal correlation patterns

Multiple factors regulate urban land surface temperature (LST), including land cover, climate, and urban form, among which urban form is now receiving more and more attention. Some studies have discussed the planar effects of urban form on LST, whereas less concern has been devoted to the vertical structure of urban areas, which can have a significant effect on heat redistribution. In this paper, we quantify the three-dimensional building form (3DBF) effects on LST captured by Landsat thermal sensors over four seasons by using a random forest (RF) regression method. The five 3DBF factors of building density (BD), building height (BH), sky view factor (SVF), frontal area index (FAI), and building shadow (BS) are calculated within a grid. The seasonal correlation of the 3DBF factors with LST is analyzed, and seven correlation patterns are modeled for the city of Wuhan in China. The results show that: 1) The best grid scale for the building form analysis in a city such as Wuhan is 180 m. 2) The 3DBF factors have a significant effect on urban LST over the four seasons. BD, BH and BS are the season-stable factors, among which BD has a heating effect, which reaches a maximum of 3.6 °C in spring, while BS has a cooling effect, which reaches −3.4 °C in winter. There is also a transition point for BH between heating and cooling at a height of 10 m. 3) SVF and FAI are season-varying factors, in which SVF has cooling effect, except in summer, while FAI also has a cooling effect, but not in winter. These findings will help us to understand how building form affects urban surface temperature, and will provide a reference for urban policy makers and planners in the future.

The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling

Weather-related power outages affect millions of utility customers every year. Predicting storm outages with lead times of up to five days could help utilities to allocate crews and resources and devise cost-effective restoration plans that meet the strict time and efficiency requirements imposed by regulatory authorities. In this study, we construct a numerical experiment to evaluate how weather parameter uncertainty, based on weather forecasts with one to five days of lead time, propagates into outage prediction error. We apply a machine-learning-based outage prediction model on storm-caused outage events that occurred between 2016 and 2019 in the northeastern United States. The model predictions, fed by weather analysis and other environmental parameters including land cover, tree canopy, vegetation characteristics, and utility infrastructure variables exhibited a mean absolute percentage error of 38%, Nash–Sutcliffe efficiency of 0.54, and normalized centered root mean square error of 68%. Our numerical experiment demonstrated that uncertainties of precipitation and wind-gust variables play a significant role in the outage prediction uncertainty while sustained wind and temperature parameters play a less important role. We showed that, while the overall weather forecast uncertainty increases gradually with lead time, the corresponding outage prediction uncertainty exhibited a lower dependence on lead times up to 3 days and a stepwise increase in the four- and five-day lead times.

Optimization of Drone Quantity Based on Empirical Analysis in Bushfire Extinguishment

With the deterioration in the environment, extreme weather is more likely to occur. The wildfire is becoming severer in turns of frequency and magnitude, causing disastrous damage to our fragile ecosystem. In this paper, we purpose a model to identify the optimal number of drones in case of any potential threat posed by bushfire in Victoria, Australia. In this paper, we use satellite data from space agencies such as NASA and the European Space Agency to determine the initial conditions and parameters of the model. including land cover, topography, traffic development, potential reinforcements nearby, distance to water and historical wildfires. We extract the data of several modes and determine the coefficients that affect the forest-mass. According to our simulation results, the optimal expected number of SSA UAV and relay UAV is 53 and 85 respectively.

Mapping Groundwater Potential for Irrigation, by Geographical Information System and Remote Sensing Techniques: A Case Study of District Lower Dir, Pakistan

The changing climate and global warming have rendered existing surface water insufficient, which is projected to adversely influence the irrigated farming systems globally. Consequently, groundwater demand has increased significantly owing to increasing population and demand for plant-based foods especially in South Asia and Pakistan. This study aimed to determine the potential areas for groundwater use for agriculture sector development in the study area Lower Dir District. ArcGIS 10.4 was utilized for geospatial analysis, which is referred to as Multi Influencing Factor (MIF) methodology. Seven parameters including land cover, geology, soil, rainfall, underground faults (liniment) density, drainage density, and slope, were utilized for delineation purpose. Considering relative significance and influence of each parameter in the groundwater recharge rating and weightage was given and potential groundwater areas were classified into very high, high, good, and poor. The result of classification disclosed that the areas of 113.10, 659.38, 674.68, and 124.17 km2 had very high, high, good, and poor potential for groundwater agricultural uses, respectively. Field surveys for water table indicated groundwater potentiality, which was high for Kotkay and Lalqila union councils having shallow water table. However, groundwater potentiality was poor in Zimdara, Khal, and Talash, characterized with a very deep water table. Moreover, the study effectively revealed that remote sensing and GIS could be developed as potent tools for mapping potential sites for groundwater utilization. Furthermore, MIF technique could be a suitable approach for delineation of groundwater potential zone, which can be applied for further research in different areas.

Audible bats provide opportunities for citizen scientists

AbstractBat conservation has been impeded by a lack of basic information about species' distributions and abundances. Public participation in closing this gap via citizen (community) science has been limited, but bat species that produce low‐frequency calls audible to the unaided human ear provide an overlooked opportunity for collaborative citizen science surveys. Audible bats are rare in regional faunas but occur globally and can be under‐surveyed by traditional methods. During 2019–2020, we were joined by community members to conduct aural surveys and expand our knowledge of rare audible desert bats in western North America through a structured survey design broadly adaptable for practitioners across the globe where audible bats occur. Our study was integrated into a statistically robust but flexible master sample in use by the North American Bat Monitoring Program (NABat), ensuring representativeness of data contributions. We used survey results to update a Bayesian species distribution model for the rare spotted bat, Euderma maculatum, accounting for imperfect detection and including land cover occupancy predictors. Detection probability was estimated ~0.7 ± 0.1. Informative priors from a previous attempt to model E. maculatum were leveraged with the new citizen science data to support spatial predictions of occurrence previously impeded by data sparsity and which reinforced the biogeographic importance of arid cliffs and canyons. Our results are preliminary but encouraging, and future surveys can scale up through the NABat design structure and Bayesian modeling framework. We encourage future surveys to use recording devices to obtain voucher calls and double‐observer methods to address false‐positive detection errors that arise with inexperienced volunteers. Our design and model supported approach to integrating citizen science surveys into bat conservation programs can strengthen both the scientific understanding of rare species and public engagement in conservation practices.

Spatial Scaling of Gross Primary Productivity Over Sixteen Mountainous Watersheds Using Vegetation Heterogeneity and Surface Topography

AbstractLand surface models intended for large‐scale applications are often executed at coarse resolutions, and the sub‐grid heterogeneity is usually ignored. Here, a spatial scaling algorithm that integrates the information of vegetation heterogeneity (land cover type and leaf area index) and surface topography (elevation, slope, relative azimuth (Raz) between the sun and the slope background, sky‐view factor, and topographic wetness index), was proposed to correct errors in gross primary productivity (GPP) estimates at a coarse spatial resolution. An eco‐hydrological model named BEPS‐TerrainLab was used to simulate GPP at 30 and 480 m resolutions for 16 mountainous watersheds selected globally. Results showed that an obvious improvement on GPP estimates at 480 m resolution was achieved after the correction in comparison with GPP modeled at 30 m resolution, with the determination coefficient increased by 0.38 and mean bias error reduced by 203gCm−2 yr−1. The combination of all the seven factors made the largest improvement for GPP estimation at 480 m resolution, suggesting that a larger improvement would be achieved when more factors of surface heterogeneity are considered. More specifically, our results indicated that five factors, including land cover type and leaf area index regarded as integrated outcomes of all the environmental conditions, Raz and sky‐view factor associated with radiation redistribution, and slope related to soil water redistribution, were especially important in the spatial scaling procedure. This study suggests that incorporating the information of surface heterogeneity into the spatial scaling algorithm is useful for improving coarse resolution GPP estimates over mountainous areas.

The effects of urban land cover dynamics on urban heat Island intensity and temporal trends

ABSTRACT Assessments of surface urban heat island (UHI) have focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain time period by including land cover information. In this study, we implemented a prototype approach to characterize the spatiotemporal variations of UHI using time series of Landsat land surface temperature products and annual land change information. We analyzed UHI distribution and change in Sioux Falls, South Dakota, in the north-central United States and found that the mean UHI intensity in the region was as large as 2.2°C during the period 1986–2017 with an increasing trend of 0.02°C per year within the area with a 5-km non-urban extent. The UHI intensity associated with high intensity urban land cover usually is stronger than with low intensity urban land cover. We evaluated the impact of different non-urban reference extents on UHI variation using different non-urban buffers. The result also suggests that the overall temporal trends of UHI intensity are almost the same when using a 5-km or 10-km non-urban buffer surrounding the urban core. The prototype approach provides a framework to consistently quantify UHI and monitor its change to a large geographic extent.

MONITORING MODEL OF LAND COVER CHANGE FOR THE INDICATION OF DEVEGETATION AND REVEGETATION USING SENTINEL-2

IInformation on land cover change is very important for various purposes, including the monitoring of changes for environmental sustainability. The objective of this study is to create a monitoring model of land cover change for the indication of devegetation and revegetation usingdata fromSentinel-2 from 2017 to 2018 of the Brantas watershed.This is one of the priority watersheds in Indonesia, so it is necessary to observe changes in its environment, including land cover change. Such change can be detected using remote sensing data. The method used is a hybrid between Normalized Difference Vegetation Index(NDVI) and Normalized Burn Ratio (NBR) which aims to detect land changes with a focus on devegetationand revegetation by determining the threshold value for vegetation index (ΔNDVI) and open land index (ΔNBR).The study found that the best thresholds to detect revegetation were ΔNDVI > 0.0309 and ΔNBR < 0.0176 and to detect devegetation ΔNDVI < -0.0206 and ΔNBR > 0.0314.It is concluded that Sentinel-2 data can be used to monitor land changes indicating devegetation and revegetation with established NDVI and NBR threshold conditions.

Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

Abstract. The dynamic characteristics of seasonal snow cover are critical for hydrology management, the climate system, and the ecosystem functions. Optical satellite remote sensing has proven to be an effective tool for monitoring global and regional variations in snow cover. However, accurately capturing the characteristics of snow dynamics at a finer spatiotemporal resolution continues to be problematic as observations from optical satellite sensors are greatly impacted by clouds and solar illumination. Traditional methods of mapping snow cover from passive microwave data only provide binary information at a spatial resolution of 25 km. This innovative study applies the random forest regression technique to enhanced-resolution passive microwave brightness temperature data (6.25 km) to estimate fractional snow cover over North America in winter months (January and February). Many influential factors, including land cover, topography, and location information, were incorporated into the retrieval models. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2008 and 2017 were used to create the reference fractional snow cover data as the “true” observations in this study. Although overestimating and underestimating around two extreme values of fractional snow cover, the proposed retrieval algorithm outperformed the other three approaches (linear regression, artificial neural networks, and multivariate adaptive regression splines) using independent test data for all land cover classes with higher accuracy and no out-of-range estimated values. The method enabled the evaluation of the estimated fractional snow cover using independent datasets, in which the root mean square error of evaluation results ranged from 0.189 to 0.221. The snow cover detection capability of the proposed algorithm was validated using meteorological station observations with more than 310 000 records. We found that binary snow cover obtained from the estimated fractional snow cover was in good agreement with ground measurements (kappa: 0.67). There was significant improvement in the accuracy of snow cover identification using our algorithm; the overall accuracy increased by 18 % (from 0.71 to 0.84), and the omission error was reduced by 71 % (from 0.48 to 0.14) when the threshold of fractional snow cover was 0.3. The experimental results show that passive microwave brightness temperature data may potentially be used to estimate fractional snow cover directly in that this retrieval strategy offers a competitive advantage in snow cover detection.

RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification

Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe prediction and management, and climate-change estimation. Big remote sensing data are characterized by different types of resolutions (radiometric, spatial, spectral, and temporal), modes of imaging, and sensor types, and this range of options often makes the process of analyzing and interpreting such data more difficult. In this paper, which is the first study of its kind, we propose a novel distributed deep learning-based approach for the classification of big remote sensing images. Specifically, we propose Distributed Convolutional-Neural-Networks for handling RS image classification (RS-DCNN). The first step is to prepare the training dataset for RS-DCNN. Then, to ensure a data-parallel training on the top of the Apache Spark framework, a pixel-based convolutional-neural-network model across the big data cluster is performed using BigDL. Experiments are conducted on a real dataset covering many regions of Saudi Arabia and the results demonstrate high classification accuracy at a faster speed than other state-of-the-art classification methods.

Spatial Interconnections of Land Surface Temperatures with Land Cover/Use: A Case Study of Tokyo

As one of the most populated metropolitan areas in the world, the Tokyo Metropolitan Area (TMA) has experienced severe climatic modifications and pressure due to densified human activities and urban expansion. The surface urban heat island (SUHI) phenomenon particularly constitutes a significant threat to human comfort and geo-environmental health in TMA. This study aimed to profile the spatial interconnections between land surface temperature (LST) and land cover/use in TMA from 2001 to 2015 using multi-source spatial data. To this end, the thermal gradients between the urban and non-urban fabric areas in TMA were examined by joint analysis of land cover/use and LST. The spatiotemporal aggregation patterns, variations, and movement trajectories of SUHI intensity in TMA were identified and delineated. The spatial relationship between SUHI and the potential driving forces in TMA was clarified using geographically weighted regression (GWR) analysis. The results show that the thermal environment of TMA exhibited a polynucleated spatial structure with multiple thermal island cores. Overall, the magnitude and extent of SUHI in TMA increased and expanded from 2001 to 2015. During that time, SUHIs clustered in the compact residential quarters and redevelopment/renovation areas rather than downtown. The GWR models showed better performance than ordinary least squares (OLS) models, with Adj R2 > 0.9, indicating that the magnitude of SUHI significantly depended on its neighboring geographical setting, including land cover composition and configuration, population size, and terrain. We suggest that UHI mitigation in Tokyo should be focused on alleviating the magnitude of persistent thermal cores and controlling unstable SUHI occurrence based on partitioned or location-specific landscape design. This study’s findings have immense implications for SUHI mitigation in metropolitan areas situated in bay regions.

Object-Based land cover classification of the Vu Gia – Thu Bon river basin on the cloud computing platform

The arrival of cloud computing platform Google Earth Engine (GEE) in 2010 has brought a breakthrough for analysing and processing spatial data. Applying algorithms on this platform has overcome the limitations of commercial software while processing data in building thematic databases, including land cover data. These data are a critical factor for climate change and hydrological models. This study applied Object-based Random Forest (RF) classification in the Google Earth Engine platform to produce land cover data from Landsat 8 data of the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of artificial forest, natural forest, paddy area, urban area, rural area, bare land, and body water, with an overall accuracy Kappa of 0.70.

Drivers of distributions and niches of North American cold‐adapted amphibians: evaluating both climate and land use

Species distribution estimates are often used to understand the niche of a species; however, these are often based solely on climatic predictors. When the influences of biotic factors are ignored, erroneous inferences about range and niche may be made. We aimed to integrate climate data with a unique set of available land cover and land use data for the six cold-adapted amphibians of North America (Ambystoma macrodactylum, Anaxyrus hemiophrys, Anaxyrus boreas, Pseudacris maculata, Rana sylvatica, Rana luteiventris) to determine the relative importance of climate and non-climate drivers through the use of ecological niche models for present-day range estimates. We compared climate-only, land use-only, and combination models of climate and land use, derived from two different model selection techniques, to determine which was most likely to drive current distributions of cold-adapted amphibian species. Land use layers included land cover type, human population, vegetation type, ecoregion, and the overall human footprint. The most supported models included both climate and land use, with climate and human footprint variables having the highest permutation importance and percent contribution. Models that incorporated climate and land use data performed best as measured with AIC and AUC, although qualitatively most underestimated the northern range edge, implying potential sampling bias or locations of reduced habitat quality for these species in the northern area of the ranges. There were small differences in overall combination models dependent on the method of model selection. The overall effect sizes of landscape factors within the combination models were small except for one landscape feature: human footprint, which incorporated multiple aspects of anthropogenic change on the landscape, including human population density, travel access, and agricultural impact. This aspect of the landscape was just as important as climate, and counter to what we expected, the association was mostly positive, with a negative response only occurring at very high levels. This highlights the importance of moving beyond climate only species range estimates as land cover, specifically human impact, may be driving the patterns of species' ranges.

Understanding the Long-Term Changes in Groundwater Level—A Tale of Highly Urbanized City

The rapid drop in the groundwater level has raised concerns in the sustainable water management plan in many cities of the world. It is important to understand the underlying factors of such depletion. To improve knowledge of the changes in groundwater, this study investigates a long-term perspective of urbanization, and evaluates the influence of several factors including land cover, water use, and abstraction. Multi-year land cover maps are used to detect urbanization and land cover types. Hotspot analysis and correlation-regression are applied to assess the relationship between changes in groundwater and the driving factors. The investigations confirm that an extensive withdrawal of groundwater due to urbanization results in a progressive drop in the water level. However, the major industrial and commercial zone shows the consistent hotspot of groundwater depletion. The results indicate that groundwater abstraction and types of water use have a high influence on the changes in urban groundwater.

ОЦЕНКА ЛЕТНЕГО ПОВЕРХНОСТНОГО ГОРОДСКОГО ОСТРОВА ТЕПЛА ГОРОДА НОВОСИБИРСК ПО ДАННЫМ LANDSAT 8

An urban heat island is a phenomenon which occurs when the temperature in urban areas is higher than that in their rural surroundings. The formation of an urban heat island has a negative impact on the health of urban dwellers and the ecology of the city. We assessed the intensity of the surface urban heat island (UHISurf) in the city of Novosibirsk during the summer period from 2018 to 2020 using two categories of indicators – those based solely on differences in temperature (UHI-driven) and those including land cover type data (land-cover-driven). Data from the Landsat 8 were used to obtain information on land surface temperature. In accordance with the chosen methodology, bands 4, 5 and 10 were taken to calculate the land surface temperature. A prerequisite for satellite data selection was the absence of cloud cover over the study area. In this paper, nine buffers with a diameter of 1 km were selected, each around ground-based meteorological observation points at the Roshydromet network of stations. The results obtained indicate the presence of a stable surface urban heat island in Novosibirsk during the study period. The maximum value among all surface urban heat island intensity indicators was 23,0°C for the range indicator. Negative intensity values (up to –1,6°C) were also obtained for several cases, which is associated with a larger heating of croplands relative to urban surfaces. It is concluded that UHISurf intensity values depend on the choice of the calculation approach.

The Use of Dam Environmental Vulnerability Index (DEVI) for Assessing Vulnerability of Bengawan Solo Watershed, Indonesia

Bengawan Solo is the longest river in Java, but current conditions show that its watershed is in a critical condition. Deforestation was very intensive in the last three decades that contributed to degradation of the watershed. Other factor contributing to the degradation is dam construction. However, our knowledge on the impact of dam construction on the environment and its vulnerability is poorly understood. Here, we assessed vulnerability of the watershed based on physical properties such as existing dams, morpho-dynamic activities, and deforested area. The study aims to identify the vulnerability of the Bengawan Solo watershed based on dam environmental vulnerability index (DEVI) approach, and to analyse the dominant variable contributing to DEVI. For calculating DEVI, several data were needed including land cover, rainfall, stream water stage, soil type, stream network, and dams. The results showed that Bengawan Solo watershed had moderate to high vulnerability (60%). Moderate level was identified for Madiun and Wonogiri sub-watershed, while high level was in Cepu and Babat sub-watershed. Our findings revealed that morpho-dynamic activities as represented by sediment rate and stream water stage had contributed to the high DEVI value as in Cepu and Babat sub-watershed. Further, influence of dams in this research was not dominant implying that any improvement to the DEVI approach remains research challenges. The improvement of the approach is expected to better identify the impact of dam construction on environment, situated in other regions than Amazon, where it was firstly developed.